Azure Certification Lecture

Agenda for Today

• Internet of Things (IoT)
• Data Warehouse
• Databricks
• Artificial Intelligence and Machine Learning
• DevOps and Monitoring Tools
• Use Case Examples and Resource Creation

Internet of Things (IoT)

• IoT (Internet of Things): connecting devices (light bulbs, toasters, etc.) to the internet for data transmission.
• Azure IoT Central: a large platform for creating and managing IoT products.
• Azure IoT Hub: a more suitable platform for smaller solutions for connecting and managing IoT devices.
• Azure Sphere: a platform for building and maintaining secure IoT applications, includes both software and hardware components.
• Example: Connecting a Raspberry Pi simulator to IoT Hub.

Data Warehouse and Analytics

• Azure Synapse Analytics (formerly Azure SQL Data Warehouse): an analytical database for large data volumes, temporary storage, and analysis.
• Azure Data Lake: storage for big data, works with Apache Hadoop, Spark, and Kafka.
• Azure Databricks: a platform for analytics and machine learning based on Apache Spark.

Artificial Intelligence and Machine Learning

• Machine Learning Studio: a platform for training and deploying machine learning models.
• Cognitive Services: a set of AI APIs (speech recognition, image analysis, etc.)
• Bot Service: allows for creating intelligent bots.

Serverless Solutions

• Serverless Architecture: executing code without managing servers, examples - Azure Functions, Azure Logic Apps.
• Azure Functions: executes functions in response to events.
• Azure Logic Apps: allows creating orchestrations and automating workflows.

DevOps and Management

• Azure DevOps: a tool for managing development teams, CI/CD, testing, and deployment.
• GitHub Actions: automating CI/CD processes directly from GitHub repositories.
• Azure Test Labs: creating test environments for solution validation.

Management and Monitoring Tools

• Azure Portal: a web interface for managing resources.
• Azure PowerShell and CLI: command-line interfaces for automating and managing resources from local machines.
• Azure Resource Manager (ARM): infrastructure layer managing deployment and management of assets in Azure.
• Azure Monitor: monitoring and alerting center for Azure resources, integrates with Log Analytics, Application Insights.
• Azure Service Health: monitoring the status of Azure services and regions, notifications of issues and planned maintenance.

Practical Examples

• Creating a resource using an ARM template.
• Creating a resource using PowerShell.
• Creating a resource using CLI.

Conclusion

• Azure provides a rich set of tools for management, monitoring, and developing solutions.
• It is important to study and integrate various services for optimal performance.
• Resource state monitoring and security are key aspects of successful management in Azure.